ABSTRACT The goal of this project is to facilitate the development and evaluation of statistical methods for identifying and characterizing the genetic contribution to complex diseases and their precursors and risk factors. We propose to pursue this goal by continuing the organization of the Genetic Analysis Workshops (GAWs), which began in 1982. The Genetic Analysis Workshops are a collaborative effort among genetic epidemiologists and statistical geneticists to evaluate and compare genetic analysis methods. For each GAW, topics are chosen for their relevance to current analytical issues in genetic epidemiology, and sets of real and computer-simulated data are distributed to investigators worldwide. Participants submit the results of their analyses, which are discussed and compared at a 3 1/2 day meeting. Participation at GAWs has increased tremendously, from fewer than 30 at GAW1 in 1982 to 272 at GAW17. In the current grant period GAW16 (2008) and GAW17 (2010) were held. The GAW17 proceedings are currently in press with publication anticipated in December 2011 and planning for GAW18 has begun. During the proposed grant period, the GAW18 Proceedings will be published and two GAWs will be held: GAW19 (in 2014) and GAW20 (in 2017). Before the Workshops, participants devote months to data analysis, communicate with others who have done similar types of analyses, and plan integrated presentations. The GAW submissions invariably contain new ideas for methods to handle complex phenotypes and a number of widely used analytical techniques had their start at a GAW. Recent GAWs have included genome-wide association data and exome sequence data, giving participants an opportunity to try out new methods for localizing and characterizing variants influencing disease risk. Topics for future GAWs will be selected from among currently challenging analytical problems. Suggestions from GAW participants include methods for analyzing whole genome sequence; genome-wide methylation profiles; comparisons of study designs for next generation sequencing projects; analyzing sequence data in admixed populations; gene-environment and gene-gene interactions; and copy number variation. We also will continue to distribute real and simulated data from past GAWs with the permission of the data providers. Long after each GAW is over, investigators continue to use GAW data sets to evaluate new analytical methods and software, to estimate power and false positive rates, and to demonstrate the feasibility of statistical techniques for finding disease genes. GAW data are extensively used in grant proposals and in teaching and dissertation research.